In the future we would like to focus on methods for predicting
fine-grained emotions (e.g., grief, loathing, terror, joy) in movies.
In this work, we consider positive/negative sentiment as a stand-in
for emotions, due to the absence of in-domain labeled
datasets. Previous efforts have focused on tweets
[1]}, Youtube opinion videos
[2]}, talkshows
[3]}, and recordings of human interactions
[4]}. Preliminary experiments revealed that
transferring fine-grained emotion knowledge from other domains to ours
leads to unreliable predictions compared to sentiment which is more
stable and improves trailer generation performance. Avenues for future
work include new emotion datasets for movies, as well as emotion
detection models based on textual and audiovisual cues.
